Automated image processing has been the subject of extensive research for many decades, and image-processing methods have been widely and advantageously applied to many problem domains. With the advent of inexpensive digital cameras and inexpensive and widely available means for transferring digital images among consumers and for transmitting digital images and video through the Internet to consumers, image-processing components and systems are increasingly frequently included in modern digital cameras, personal computers, workstations, and other consumer electronic devices and systems.
There are many different types of image-processing methods and systems, and image-processing systems vary in complexity and expense, from industrial image-processing systems, executing on large, distributed, high-throughput computer systems, to small collections of image-processing routines executed within hand-held consumer-electronics devices. Many image-processing methods and systems are devoted to rendering, restoration, and enhancement of two-dimensional photographic images and video frames, while other types of image-processing systems are directed to automated recognition of objects and events in digital images and video streams for a wide variety of different purposes, including image and video classification, storage, and retrieval, automated surveillance, automated monitoring and tracking systems, and a variety of other purposes.
In both restoration and enhancement of images and video frames as well as for automated visual systems, a capability for identifying and labeling particular types of regions and features within images is generally useful and can even critical, in certain applications. Many different approaches and methods have been developed, for example, for recognizing human faces in images and regions of images corresponding to sky and other commonly encountered objects and features. As in most computational processes, there are fundamental tradeoffs between computational overhead and processing time for applying image-processing techniques and the correctness, precision, and robustness of automatic region and object identification within images and video frames. In certain cases, high precision and correctness is required, and the time of analysis and processing is of less importance. In such cases, one or more computationally expensive and time-consuming analytical methods can be applied, and the analysis and processing may be repeated and results from different techniques compared and contrasted in order to achieve the greatest possible correctness and precision. In other cases, such as in real-time processing of video frames within consumer-electronics devices, the amount of computational overhead and time that can be devoted to image processing is quite constrained, and a primary goal is to achieve the highest precision and correctness obtainable under severe time and computing constraints. Image-processing researchers, manufacturers and vendors of consumer electronics devices and image-processing systems, and users of consumer electronics devices, and consumers of images and image-related services all recognize the need for continued development of image-processing methods and systems that can improve the correctness and precision of automated object and feature recognition as well as provide automated object and feature recognition in a computationally efficient and time-efficient manner.